2. M.PHIL (MATHEMATICS) By Munir Hussain Supervised By Dr. Muhammad Sabir

3.  Introduction to Model Problems  Methodology  Results and Discussions  Conclusion  References

4.  The Black-Scholes equation introduced 1973 by F. Black and M. Scholes and extended the same year by R.C. Merton, is an often solved equation to determine the arbitrage free price of an option. Two common types of options are the European option that only can be exercised at the time of maturity T and the American option that can be exercised at any time t T. This extra freedom in the American option will be reflected in the price. For American options this possibility of using the option before T is often referred to as the early exercise constraint. Because of this feature the problem of pricing American options will have an internal free boundary.

5.  In Mathematical Finance, the black scholes equation is a Partial differential equation governing the price evolution of a Eurpean call or Eurpean put option under the Black Schole model.

7.  A binary option is a financial exotic option in which the pay-off is either some fixed monetary amount or nothing at all. (DIGITAL OPTION)  A European option is a type of put or call option that can be exercised only on its expiration date.(European option )  An option to buy assets at an agreed price on or before a particular date.(CALL OPTION)  An option to sell assets at an agreed price on or before a particular date.(PUT OPTION)  An American option is an option that can be exercised any during its life and not only at its expiration. American option allow the holders to exercise the option at any time. (An American option)

12. METHODOLOGY

13.  Finite Differences (FD) approximate derivatives by combining nearby function values using a set of weights. Several different algorithms are available for calculating such weights. Important applications (beyond merely approximating derivatives of given functions) include linear multistep methods (LMM) for solving ordinary differential equations (ODEs) and finite difference methods for solving partial differential equations (PDEs).

15.  Here we explain in short the operator splitting technique used by Ikonen and Toivanen. Assume that we have the LCP problem formulated. The operator splitting is based on a reformulation of the problem using an auxiliary variable that keeps track of the free boundary. The variable can be used to find the location of the free boundary during or after the computations.  We have used to find where local time-stepping should be used. Omitting boundary conditions we get

17.  Crank-Nicolson and a Runge-Kutta time- stepping scheme with constant timesteps. Here we will use only the BDF2 scheme but allow for changing time-steps. This is because we will combine the splitting technique with the adaptive time-stepping algorithm we have previously used in [17] and [25].  The time-stepping scheme BDF2 with variable time-steps for a discrete vector u is

21. IMPLIMENTATION OF SCHEME TO MODEL PROBLEM

22.  The original problem is stated as a final value problem with a terminal condition at time T. As in most literature we have here used a transformation of the timescale, t = T − ˜t (where ˜t is the original time), so that the problem becomes a forward problem with a known initial condition. This makes standard texts on time- integration for an ordinary differential equation (ODE) applicable. Let us first give the transformed Black-Scholes equation for the arbitrage free price F(x, t) of the European option,

26.  We have also studied the numerical pricing of American options under stochastic volatility. The model problem we have used is the American put option using Heston’s model. The stochastic volatility model by Heston is given by

31.  In this paper we will price the American option with constant and stochastic volatility and use the adaptive techniques developed in Three variants of an adaptive time-stepping scheme with the backward differentiation formula of order 2 (BDF2) are considered, the standard scheme, a scheme modified at the free boundary and one with local time-stepping at the free boundary.  The three schemes will be denoted by x1a, x1b and x2 respectively where x should be replaced by either C (for constant volatility) or S (for stochastic volatility) depending on what case we study, see Tab. 1.

33. RESULTS OF CONSTANT VOLATILITY

38. RESULTS FOR STOCHASTIC VOLATILITY

41.  For constant volatility we study continuity of derivatives of the solution over  the free boundary. We conclude that the second derivatives are not continuous.  Studying the numerical approximation of the time derivative near the  free boundary, when BDF2 is used, we find that the order of accuracy is reduced  from second to first order which motivates the use of a first order method  there. With constant volatility we see that the time-stepping algorithm C1b  gives similar time-steps and performance as C2 but without the control of the  local discretization error C2 has. With stochastic volatility S1a and S1b give  similar errors but S1b is faster due to smoother time-step selection. S2 is slower  than both S1a and S1b and produces larger errors. However, it seems to control  the local discretization errors better. The reason for the larger errors is probably  due to the restriction in the number of steps with local time-stepping and  the fact that local time-stepping will be used in a small area away from the free  boundary with the technique used.

42.  [1] Y. Achdou and O. Pironneau. Computational methods for option pricing. SIAM, 2005. In the series Frontiers in applied mathematics.  [2] F. Black and M. Scholes. The pricing of options and corporate liabilities.  Journal of Political Economy, 81:637–659, 1973.  [3] P.P. Boyle, M. Broadie, and P. Glasserman. Monte Carlo methods for  security pricing. Journal of Economic Dynamics & Control, 21:1267–1321,  1997.  [4] M.J. Brennan and E.S. Schwartz. The valuation of American put options.  The Journal of Finance, 32(2):449–462, 1977.  [5] X. Chen and J. Chadam. A mathematical analysis for the optimal exercise  boundary of American put option. Preprint., 2000.

43.  [6] N. Clarke and K. Parrott. The multigrid solution of two-factor American put options. Technical Report 96-16, Oxford Comp. Lab, 1996.  [7] N. Clarke and K. Parrott. Multigrid for American option pricing with  stochastic volatility. Applied Mathematical Finance, 6:177–195, 1999.  [8] P. Glasserman. Monte Carlo Methods in Financial Engineering. Applications of mathematics: Stochastic modeling and applied probability. Springer Verlag, New York, 2004.  [9] G.H. Golub and C.F. Van Loan. Matrix Computations. The John Hopkins University Press, 3rd edition, Baltimore, Maryland 1996.

44. QUESTIONS…..???